I am often presented with a task of predicting monthly revenues of retail outlets. Say I have a training set of N outlets, each associated with a series of historical monthly revenues (target) and a set of features which are time-independent (e.g. location (and all associated geofeatures), outlet type, trading area etc.) Then I have a test set of M outlets for which I need to predict monthly revenues for a specified time period (e.g. Q1-Q3 2019).
The usual approach I take consists of adjusting historical revenues on the training set for trend, seasonality and inflation, then take the average for each outlet and use it as a target, train my regression model (could be anything but mostly LightGBM), get predictions for the test set. These predictions are, again, averages for each outlet adjusted for trend, seasonality and inflation, so I need to deadjust them to obtain monthly predictions. So basically the scheme is: monthly historical revenues -> adjusted average as a target -> adjusted average as a prediction -> reconstructed monthly predictions (hope this makes sense).
The whole approach is pretty tedious and feels unnatural. I wonder whether there are frameworks that would allow the use of historical monthly revenues "as is" as a target and produce predictions for the specified time range.
There is, of course, panel regression, but 1) my features don't change over time so the idea seems superficial and 2) gradient boosting in panel regressions isn't exactly a hot topic so implementations are rare (if any exist).